class_docstr: The G2P model evaluation wrapper
  
  Arguments
  ---------
  hparams: dict
      the dictionary from a parsed hyperparameter file
  device: str
      the device identifier
  model_state: dict
      a pre-loaded model state for a "warm start" if applicable
      - could be useful if hyperparameters have changed, but
      the same model can be reused from one run to the next
docstr: Adds the required delimiter characters to a word
  
  Arguments
  ---------
  word: torch.Tensor
      a tensor representing a word
docstr_tok: ["Adds","the","required","delimiter","characters","to","a","word","Arguments","--","--","--","--","-","word",":","torch.Tensor","a","tensor","representing","a","word"]
code: def _add_delimiters(self, word):
      """Adds the required delimiter characters to a word
  
      Arguments
      ---------
      word: torch.Tensor
          a tensor representing a word
      """
      if self.grapheme_sequence_mode == "bos":
          word = torch.cat([self._bos, word])
      elif self.grapheme_sequence_mode == "eos":
          word = torch.cat([word, self._eos])
      return word
  
imports: from hyperpyyaml import load_hyperpyyaml
  from speechbrain.dataio.batch import PaddedBatch
  from speechbrain.lobes.models.g2p.dataio import get_sequence_key
  from speechbrain.utils import hpopt
  from speechbrain.wordemb.util import expand_to_chars
  from train import dataio_prep, load_dependencies
  from types import SimpleNamespace
  from tqdm.auto import tqdm
  import math
  import itertools
  import speechbrain
  import torch
  import sys
  import logging
label_desc: Use image node_id 12 for calling the G2PEvaluator obj's underlying member method code with example usage: obj._add_delimiters(word) and returns: word